ML Ops Engineer

CMC MARKETS PLC; City of Westminster; On-site; GBP 70,000 - 100,000; Full time

Job summary

CMC Markets PLC is seeking an ML Ops Engineer to build and operate platform capabilities that move ML models from experimentation to reliable production services. You will own automation, deployment, observability, and controls around the ML lifecycle, collaborating with research, software, platform and product teams.

You will design repeatable workflows for training, validation, deployment and retraining, productionise models with versioning, and create CI/CD pipelines for ML systems.

Qualifications

• 3-7 years of professional experience in MLOps or ML platform engineering.
• Strong production Python skills with clean APIs and testing.
• Experience deploying models in production and monitoring.

Responsibilities

• Build repeatable workflows for model training, validation, deployment and retraining.
• Productionise models with versioning, model registry integration and deployment automation.
• Design CI/CD pipelines for ML systems, including testing, validation and release controls.
• Manage experiment tracking and reproducibility across teams.
• Build tooling to support multiple models and engineering groups.
• Collaborate with research, engineering, platform and product teams.

Skills

Python

PyTorch

MLOps

CI/CD

Observability

Containers

IaC

SRE/DevOps

Communication

Tools

MLflow

Kubeflow

Docker

Kubernetes

Airflow

Job description

We're hiring an ML Ops Engineer to build and operate the platform capabilities that take machine-learning models from experimentation into reliable production services. You'll own the automation, deployment, observability and operational controls around the ML lifecycle, working closely with research engineers, software engineers, platform teams and product teams. This is not a research role. It is a hands‑on engineering role focused on making ML systems reproducible, scalable, secure and dependable, from model packaging and release through to serving, monitoring, retraining and incident response.

• Build repeatable workflows for model training, validation, promotion, deployment and retraining.
• Productionise models through packaging, versioning, model registry integration, deployment automation and safe rollback.
• Design CI/CD pipelines for ML systems, including automated testing, validation, release controls and environment promotion.
• Manage experiment tracking, model metadata and reproducibility across research and production.
• Build reusable tooling and platform capabilities that support multiple models and engineering teams.

Model serving and observability

• Deploy and operate batch and online inference services in containerised cloud environments.
• Define and meet availability, latency, throughput and recovery objectives for ML services.
• Monitor service health, infrastructure, data-quality signals, data drift, prediction drift and model performance decay.
• Establish dashboards, alerting and operational runbooks so failures are detected and resolved quickly.
• Support automated or controlled retraining, model promotion, rollback and model retirement.
• Debug production issues across model, application, infrastructure and critical data‑dependency layers.

Reliability, security and engineering quality

• Improve system robustness, scalability and cost efficiency through automation, observability and infrastructure as code.
• Write production‑grade Python for long-running services, deployment tooling and ML workflows.
• Establish testing, validation, release and incident-management practices for ML systems.
• Collaborate with platform, security and data engineering teams on reliable model inputs, access controls, secrets, resilience and compliance.
• Make explicit trade-offs between research flexibility, delivery speed, operational risk and production stability.

Additional responsibilities

• Maintain personal/professional development to meet the changing demands of the role, including all relevant regulatory and legislative training.
• When dealing with all customers, clients or colleagues ensure that we provide a clear, fair and consistent high quality service that presents a professional and positive image of CMC Markets.
• Take all reasonable steps to ensure appropriate confidentiality.
• Undertake such other duties, training and/or hours of work as may be reasonably required and which are consistent with the general level of responsibility of this role, Observability: Metrics, logging, tracing, alerting and monitoring across model, service and platform layers.
• Cloud: Managed compute, storage and networking, with a provider-agnostic mindset The technology stack will evolve. We value engineers who understand why systems are designed in particular ways and can adapt as requirements and tools change. Why this role matters Machine-learning models only create value when they are correct, observable and dependable in production. This role is responsible for making that happen. You'll reduce the gap between promising experiments and production systems that can be trusted by downstream products and customers. Your work will improve the reliability, speed and scalability of the ML platform across the organisation. If you care about operational clarity, robust engineering and building ML systems that do not silently fail, this role gives you direct leverage over the success of our machine-learning capabilities.

Nice to have

• Prior ownership of model monitoring, drift detection or automated retraining.
• Familiarity with model registries, feature stores and offline/online feature-consistency challenges.
• Experience supporting multiple models, services or teams on a shared ML platform.
• Exposure to regulated or high-reliability production environments.
• Experience with PyTorch or similar ML frameworks and model‑serving technologies.

Technology environment

• Language: Python ML tooling: PyTorch or similar frameworks, experiment tracking and model registries Workflow orchestration: ML workflows for training, validation, deployment and retraining

• 3-7 years' professional experience in MLOps, ML platform engineering, ML infrastructure, backend engineering, DevOps or SRE.
• Strong production Python skills, including clean APIs, testing, performance awareness and maintainable services.
• Experience deploying, serving and operating machine-learning models in production environments.
• Practical understanding of the ML lifecycle, including training, validation, inference, model release, monitoring and retraining.
• Experience designing CI/CD workflows and release processes for ML or other production software systems.
• Hands‑on experience with at least one workflow or orchestration system used for ML training, validation or deployment.
• Comfort working with cloud infrastructure, containers, infrastructure as code and service networking.
• Strong understanding of observability, monitoring, alerting, incident response and common failure modes in ML systems.
• Ability to reason about system design, reliability and operational trade-offs-not just individual tools.
• Clear communication skills and the ability to work effectively with research, engineering, platform, security and product teams.